预处理的限制
Limits of Preprocessing
摘要：
我们提出了第一个理论分析的权力多项式时间预处理的重要组合问题，从各个领域的人工智能。我们从约束满足、全局约束、可满足性、非单调性和贝叶斯推理等方面考虑问题。我们表明，在复杂性理论假设下，所考虑的问题都不能通过多项式时间预处理减少到其大小在输入的结构问题参数（如诱导宽度或后门大小）中为多项式的问题核。我们的研究结果提供了一个坚实的理论边界多项式时间预处理算法的性能所考虑的问题。
英文摘要：
We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity theoretic assumption, none of the considered problems can be reduced by polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, such as induced width or backdoor size. Our results provide a firm theoretical boundary for the performance of polynomial-time preprocessing algorithms for the considered problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
